CN111459828A - Non-functional test evaluation method and device for software version - Google Patents
Non-functional test evaluation method and device for software version Download PDFInfo
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Abstract
The invention provides a method and a device for evaluating non-functional tests of software versions, wherein the method comprises the following steps: obtaining the evaluation characteristics of the software version to be produced; inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label. The device is used for executing the method. The method and the device for evaluating the non-functional test of the software version improve the evaluation efficiency of the non-functional test of the software version.
Description
Technical Field
The invention relates to the technical field of software development, in particular to a method and a device for evaluating non-functional tests of software versions.
Background
Software testing is an essential important link in the software development process, and can be divided into functional testing and non-functional testing.
With the continuous innovation of financial services and the high-speed development of internet technology, the financial software system is developed like spring bamboo blossoming after rain. In order to comprehensively ensure the successful online production of the production versions of the financial software, each change point of the to-be-produced version needs to be evaluated, and whether non-functional tests, such as a performance pressure test, a high availability test, a high reliability test, maintainability, expandability and the like, need to be performed on the version is judged. The non-functional test of the production versions needs to invest more resources, and the non-functional test is not needed for each production version. Therefore, it is necessary to perform a non-functional evaluation on the change point of each production version to determine whether a non-functional test is necessary. In the prior art, the whole judgment process of the non-functional test depends on manual evaluation, the requirement on professional background of an evaluator is extremely high, and the problems of low evaluation efficiency, low accuracy and the like exist.
Disclosure of Invention
For solving the problems in the prior art, embodiments of the present invention provide a method and an apparatus for non-functional test evaluation of a software version, which can at least partially solve the problems in the prior art.
In one aspect, the present invention provides a method for non-functional test evaluation of a software version, including:
obtaining the evaluation characteristics of the software version to be produced;
inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
In another aspect, the present invention provides a device for evaluating a non-functional test of a software version, including:
the acquiring unit is used for acquiring the evaluation characteristics of the software version to be produced;
the evaluation unit is used for inputting the evaluation characteristics into a risk evaluation model and outputting the evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
In another aspect, the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method for evaluating a non-functional test of a software version according to any of the above embodiments are implemented.
In yet another aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for non-functional test evaluation of software versions according to any of the above embodiments.
The method and the device for evaluating the non-functional test of the software version can acquire the evaluation characteristics of the software version to be produced, input the evaluation characteristics into the risk evaluation model, and output the evaluation result of the software version to be produced, thereby realizing the automation of the evaluation of the non-functional test of the software version and improving the evaluation efficiency of the non-functional test of the software version.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a flowchart illustrating a method for non-functional test evaluation of a software version according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating a method for evaluating a non-functional test of a software version according to another embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a non-functional test evaluation apparatus for software version according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a non-functional test evaluation apparatus for software version according to another embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a non-functional test evaluation apparatus for software version according to still another embodiment of the present invention.
Fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Fig. 1 is a schematic flow chart of a method for evaluating a non-functional test of a software version according to an embodiment of the present invention, and as shown in fig. 1, the method for evaluating a non-functional test of a software version according to an embodiment of the present invention includes:
s101, obtaining the evaluation characteristics of the software version to be produced;
specifically, for the software version to be delivered, the evaluation feature of the software version to be delivered may be input into a server, and the server may obtain the evaluation feature of the software version to be delivered. The evaluation feature is used to evaluate whether the software to be produced needs to be subjected to a non-functional test, and is set according to actual needs. The execution subject of the non-functional test evaluation method of the software version provided by the embodiment of the invention includes but is not limited to a server.
For example, the evaluation feature may be obtained based on the basic feature information and the non-functional index feature variation information of the software version to be commissioned. The basic characteristic information can comprise version types, importance degrees and the like of software versions to be produced, and the version types can be divided into new construction, new service, service function change, existing system popularization, existing system architecture modification, existing system infrastructure change and the like. The degree of importance may be classified by rank as A +, A, A-, B +, B, B-, C, etc. The non-functional characteristic indexes can comprise types of performance capacity, availability, reliability, maintainability, expandability, consistency, safety and the like, and each type of non-functional characteristic indexes can be further subdivided according to actual needs.
The basic characteristic information and the non-functional index characteristic change information of the software version to be produced can be collected by a tester and input into the server according to a set template, and corresponding characteristic values are simultaneously input as the evaluation characteristics when the basic characteristic information and the non-functional index characteristic change information are input. After the evaluation features are input, the server can verify the integrity and the validity of the evaluation features, wherein the integrity is to check whether all the evaluation features are input according to the number of preset evaluation features, if the evaluation features are lacked, the server can prompt the completion of the evaluation features, and the validity is to check the data format of the evaluation features, wherein the data format of the evaluation features is preset, and if the input data of the evaluation features do not meet the format requirements, the server can prompt the input format errors of the evaluation features.
S102, inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
Specifically, after obtaining the evaluation features of the software version to be delivered, the server may input the evaluation features into a risk evaluation model, and may output the evaluation result of the software version to be delivered through the processing of the risk evaluation model, where the evaluation result may be represented by a risk label, and different risk labels represent different risk levels, and a higher risk level indicates that a problem is more likely to be generated without performing a non-functional test. The risk assessment model is obtained after training based on software version assessment sample training feature data and predetermined risk labels, and each assessment sample training feature data included in the software version assessment sample training feature data corresponds to one risk label. The risk label corresponding to the evaluation sample training feature data is predetermined based on the evaluation sample training feature data.
The method for evaluating the non-functional test of the software version can acquire the evaluation characteristics of the software version to be produced, input the evaluation characteristics into the risk evaluation model, and output the evaluation result of the software version to be produced, thereby realizing the automation of the evaluation of the non-functional test of the software version and improving the evaluation efficiency of the non-functional test of the software version. In addition, the risk that the software version to be produced is not subjected to the non-functional test can be objectively evaluated through the evaluation characteristics, the evaluation accuracy of the non-functional test of the software version is improved, a large amount of manpower can be saved, and the evaluation cost of the non-functional test is reduced.
Fig. 2 is a flowchart of a method for non-functional test evaluation of a software version according to another embodiment of the present invention, and as shown in fig. 2, based on the foregoing embodiments, further training the risk evaluation model based on a training feature of a software version evaluation sample and a predetermined risk label includes:
s201, dividing training characteristics of the software version evaluation sample into a training set and a verification set;
specifically, historical evaluation features of a preset number of software versions can be collected and obtained as the training features of the software version evaluation samples and input into the server, and the server can obtain the training features of the software version evaluation samples and divide the training features of the software version evaluation samples into a training set and a validation set. The preset number is set according to actual needs, and the embodiment of the invention is not limited.
For example, the software version evaluation sample training feature data includes Q evaluation sample training feature data, 0.7Q evaluation sample training feature data is classified into the training set, and 0.3Q evaluation sample training feature data is classified into the validation set. Each evaluation sample training feature data would correspond to a risk label.
S202, training to obtain a risk assessment model to be determined according to the training set, the risk labels corresponding to the training set and a decision tree training model; wherein the decision tree training model is pre-established;
specifically, the server inputs evaluation sample training feature data in the training set and risk labels corresponding to the evaluation sample training feature data to a decision tree training model, and obtains a risk evaluation model to be determined through training. Wherein, the decision tree training model is established in advance. The specific training process of the model is the prior art, and is not described herein.
For example, the decision tree training model is created based on a decision tree induction algorithm and rules in a rule base, each path from a root node to a leaf node of the decision tree corresponds to one rule, and the rules are used as splitting conditions until an evaluation result of different evaluation feature combinations is obtained when splitting cannot be performed. Wherein the decision tree covers all types corresponding to the evaluation features. The rule base is preset.
S203, verifying the risk assessment model to be determined according to the verification set and the risk label corresponding to the verification set;
specifically, after obtaining the risk assessment model to be determined, the server may verify the effect of the risk assessment model to be determined through the verification set, input the assessment sample training feature data of the verification set and the risk label corresponding to the assessment sample training feature data to the risk assessment model to be determined, obtain the assessment result of each assessment sample training feature data of the verification set, compare the assessment result of each assessment sample training feature data in the verification set with the risk label corresponding to each assessment sample training feature data, may obtain the accuracy of the risk assessment model to be determined, if the accuracy of the risk assessment model to be determined is greater than or equal to the accuracy threshold, the risk assessment model to be determined passes verification, if the accuracy of the risk assessment model to be determined is less than the accuracy threshold, the risk assessment model to be determined cannot be validated. The accuracy threshold is set according to actual experience, and the embodiment of the present invention is not limited.
And S204, if the risk assessment model to be determined is judged to pass the verification, taking the risk assessment model to be determined as the risk assessment model.
Specifically, after verifying the risk assessment model to be determined, the server may determine whether the risk assessment model to be determined passes verification, and if the risk assessment model to be determined passes verification, the server may use the risk assessment model to be determined as the risk assessment model. And if the risk assessment model to be determined is not verified, performing model training again.
On the basis of the above embodiments, further, the decision tree training model is created by rules in a rule base based on a decision tree induction algorithm; wherein the rule base is preset.
Specifically, the server creates a decision tree training model through rules in a rule base based on a decision tree induction algorithm, the decision tree training model comprises all evaluation features, the rules in the rule base are converted into splitting conditions, each path from a root node of the decision tree to a leaf node corresponds to one rule in the rule base, when training is carried out, traversal operation is carried out on the decision tree from the root node of the decision tree until traversal of training feature data of one evaluation sample of a training set is completed, and finally the reached leaf node is the evaluation result corresponding to the training feature data of the evaluation sample. Each rule of the rule base corresponds to at least one evaluation feature, and is set according to actual needs, which is not limited in the embodiment of the invention.
For example, for the evaluation feature: and the transaction change amount is determined whether the transaction change amount is larger than 5% or not according to a corresponding rule, if the transaction change amount is larger than or equal to 5%, the transaction change amount is converted to the next leaf node, and if the transaction change amount is smaller than 5%, the transaction change amount is converted to another leaf node.
On the basis of the above embodiments, further, the risk label includes six risk levels of high risk, medium risk, low risk and no risk; accordingly, the method further comprises:
and if the evaluation result is judged to be high risk, medium high risk or medium risk, prompting to perform non-functional test.
In particular, the risk label may include six risk levels of high risk, medium low risk, and no risk. And the evaluation result of the software version to be produced is one of the six risk levels. And after obtaining the evaluation result of the software version to be produced, the server compares the evaluation result with the six risk levels, and if the evaluation result is high risk, medium risk or medium risk, the server indicates that the software version to be produced needs to be subjected to non-functional test, so that the server can prompt the non-functional test. If the evaluation result is medium-low risk, low risk and no risk, the software version to be produced can not be subjected to non-functional test.
Fig. 3 is a schematic structural diagram of a non-functional test evaluation apparatus of a software version according to an embodiment of the present invention, and as shown in fig. 3, the non-functional test evaluation apparatus of a software version according to an embodiment of the present invention includes an obtaining unit 301 and an evaluating unit 302, where:
the obtaining unit 301 is configured to obtain an evaluation feature of a software version to be produced; the evaluation unit 302 is configured to input the evaluation features into a risk evaluation model, and output an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
Specifically, for the software version to be delivered, the evaluation feature of the software version to be delivered may be input into the server, and the obtaining unit 301 may obtain the evaluation feature of the software version to be delivered. The evaluation feature is used to evaluate whether the software to be produced needs to be subjected to a non-functional test, and is set according to actual needs.
After obtaining the evaluation features of the software version to be commissioned, the evaluation unit 302 may input the evaluation features into a risk evaluation model, and after processing by the risk evaluation model, may output an evaluation result of the software version to be commissioned, where the evaluation result may be represented by a risk label, and different risk labels represent different risk levels, and a higher risk level indicates a higher possibility of generating a problem without performing a non-functional test. The risk assessment model is obtained after training based on software version assessment sample training feature data and predetermined risk labels, and each assessment sample training feature data included in the software version assessment sample training feature data corresponds to one risk label. The risk label corresponding to the evaluation sample training feature data is predetermined based on the evaluation sample training feature data.
The non-functional test evaluation device for the software version provided by the embodiment of the invention can acquire the evaluation characteristics of the software version to be produced, input the evaluation characteristics into the risk evaluation model, and output the evaluation result of the software version to be produced, thereby realizing the automation of the non-functional test evaluation of the software version and improving the evaluation efficiency of the non-functional test of the software version. In addition, the risk that the software version to be produced is not subjected to the non-functional test can be objectively evaluated through the evaluation characteristics, the evaluation accuracy of the non-functional test of the software version is improved, a large amount of manpower can be saved, and the evaluation cost of the non-functional test is reduced.
Fig. 4 is a schematic structural diagram of a non-functional test evaluation apparatus of a software version according to another embodiment of the present invention, and as shown in fig. 4, on the basis of the foregoing embodiments, further, the non-functional test evaluation apparatus of a software version according to the embodiment of the present invention further includes a dividing unit 303, a training unit 304, a verification unit 305, and a determination unit 306, where:
the dividing unit 303 is configured to divide the training features of the software version evaluation sample into a training set and a validation set; the training unit 304 is configured to train to obtain a risk assessment model to be determined according to the training set, the risk labels corresponding to the training set, and a decision tree training model; wherein the decision tree training model is pre-established; the verification unit 305 is configured to verify the risk assessment model to be determined according to the verification set and the risk label corresponding to the verification set; the determining unit 306 is configured to use the risk assessment model to be determined as the risk assessment model after it is judged and known that the risk assessment model to be determined passes verification.
Specifically, historical evaluation features of a preset number of software versions can be collected and obtained as the training features of the software version evaluation samples and input into the dividing unit 303, and the dividing unit 303 can obtain the training features of the software version evaluation samples and divide the training features of the software version evaluation samples into a training set and a verification set. The preset number is set according to actual needs, and the embodiment of the invention is not limited.
The training unit 304 inputs the evaluation sample training feature data in the training set and the risk label corresponding to the evaluation sample training feature data to the decision tree training model, and obtains the risk evaluation model to be determined through training. Wherein, the decision tree training model is established in advance. The specific training process of the model is the prior art, and is not described herein.
After obtaining the risk assessment model to be determined, the verification unit 305 may verify the effect of the risk assessment model to be determined through the verification set, input the assessment sample training feature data of the verification set and the risk label corresponding to the assessment sample training feature data to the risk assessment model to be determined, obtain the assessment result of each assessment sample training feature data of the verification set, compare the assessment result of each assessment sample training feature data in the verification set with the risk label corresponding to each assessment sample training feature data, may obtain the accuracy of the risk assessment model to be determined, if the accuracy of the risk assessment model to be determined is greater than or equal to the accuracy threshold, the risk assessment model to be determined passes verification, if the accuracy of the risk assessment model to be determined is less than the accuracy threshold, the risk assessment model to be determined cannot be validated. The accuracy threshold is set according to actual experience, and the embodiment of the present invention is not limited.
After verifying the to-be-determined risk assessment model, the determining unit 306 determines whether the to-be-determined risk assessment model passes the verification, and if the to-be-determined risk assessment model passes the verification, the to-be-determined risk assessment model is used as the risk assessment model. And if the risk assessment model to be determined is not verified, performing model training again.
Fig. 5 is a schematic structural diagram of a non-functional test evaluation apparatus of a software version according to yet another embodiment of the present invention, as shown in fig. 5, and based on the foregoing embodiments, further, the decision tree training model is created by rules in a rule base based on a decision tree generalization algorithm; wherein the rule base is preset.
Specifically, a decision tree training model is created through rules in a rule base based on a decision tree induction algorithm, the decision tree training model comprises all evaluation features, the rules in the rule base are converted into splitting conditions, each path from a root node of the decision tree to a leaf node corresponds to one rule in the rule base, when training is carried out, traversal operation is carried out on the decision tree from the root node of the decision tree until traversal of one evaluation sample training feature data of a training set is completed, and finally the reached leaf node is an evaluation result corresponding to the evaluation sample training feature data. Each rule of the rule base corresponds to at least one evaluation feature, and is set according to actual needs, which is not limited in the embodiment of the invention.
On the basis of the above embodiments, further, the risk label includes six risk levels of high risk, medium risk, low risk and no risk; correspondingly, the device for evaluating the non-functional test of the software version provided by the embodiment of the invention further comprises:
the prompting unit 307 is configured to prompt to perform a non-functional test after determining that the evaluation result is a high risk, a medium high risk, or a medium risk.
In particular, the risk label may include six risk levels of high risk, medium low risk, and no risk. And the evaluation result of the software version to be produced is one of the six risk levels. After obtaining the evaluation result of the software version to be commissioned, the prompting unit 307 compares the evaluation result with the six risk levels, and if the evaluation result is high risk, medium risk or medium risk, it indicates that the software version to be commissioned needs to be subjected to a non-functional test, and can prompt the non-functional test. If the evaluation result is medium-low risk, low risk and no risk, the software version to be produced can not be subjected to non-functional test.
The embodiment of the apparatus provided in the embodiment of the present invention may be specifically configured to execute the processing flows of the above method embodiments, and the functions of the apparatus are not described herein again, and refer to the detailed description of the above method embodiments.
Fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device may include: a processor (processor)601, a communication Interface (Communications Interface)602, a memory (memory)603 and a communication bus 604, wherein the processor 601, the communication Interface 602 and the memory 603 complete communication with each other through the communication bus 604. The processor 601 may call logic instructions in the memory 603 to perform the following method: obtaining the evaluation characteristics of the software version to be produced; inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
In addition, the logic instructions in the memory 603 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising: obtaining the evaluation characteristics of the software version to be produced; inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
The present embodiment provides a computer-readable storage medium, which stores a computer program, where the computer program causes the computer to execute the method provided by the above method embodiments, for example, the method includes: obtaining the evaluation characteristics of the software version to be produced; inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description herein, reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," "an example," "a particular example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for non-functional test evaluation of a software version, comprising:
obtaining the evaluation characteristics of the software version to be produced;
inputting the evaluation characteristics into a risk evaluation model, and outputting an evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
2. The method of claim 1, wherein training the risk assessment model based on software version assessment sample training features and predetermined risk labels comprises:
dividing the training features of the software version evaluation sample into a training set and a verification set;
training to obtain a risk assessment model to be determined according to the training set, the risk labels corresponding to the training set and a decision tree training model; wherein the decision tree training model is pre-established;
verifying the risk assessment model to be determined according to the verification set and the risk label corresponding to the verification set;
and if the risk assessment model to be determined is judged to pass the verification, taking the risk assessment model to be determined as the risk assessment model.
3. The method of claim 2, wherein the decision tree training model is created by rules in a rule base based on a decision tree induction algorithm; wherein the rule base is preset.
4. The method of any one of claims 1 to 3, wherein the risk label comprises six risk levels of high risk, medium low risk, low risk and no risk; accordingly, the method further comprises:
and if the evaluation result is judged to be high risk, medium high risk or medium risk, prompting to perform non-functional test.
5. A non-functional test evaluation apparatus for a software version, comprising:
the acquiring unit is used for acquiring the evaluation characteristics of the software version to be produced;
the evaluation unit is used for inputting the evaluation characteristics into a risk evaluation model and outputting the evaluation result of the software version to be produced; the risk assessment model is obtained after training based on the software version assessment sample training feature data and the predetermined risk label.
6. The apparatus of claim 5, further comprising:
the dividing unit is used for dividing the training characteristics of the software version evaluation sample into a training set and a verification set;
the training unit is used for training to obtain a risk assessment model to be determined according to the training set, the risk labels corresponding to the training set and a decision tree training model; wherein the decision tree training model is pre-established;
the verification unit is used for verifying the risk assessment model to be determined according to the verification set and the risk label corresponding to the verification set;
and the determining unit is used for taking the risk evaluation model to be determined as the risk evaluation model after judging that the risk evaluation model to be determined passes the verification.
7. The apparatus of claim 6, wherein the decision tree training model is created by rules in a rule base based on a decision tree induction algorithm; wherein the rule base is preset.
8. The apparatus of any one of claims 5 to 7, wherein the risk label comprises six risk levels of high risk, medium low risk, low risk and no risk; correspondingly, the device further comprises:
and the prompting unit is used for prompting to perform non-functional test after judging and knowing that the evaluation result is high risk, medium and high risk or medium risk.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 4 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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| CN112346995A (en) * | 2020-12-04 | 2021-02-09 | 中信银行股份有限公司 | Construction method and device of test risk estimation model based on banking industry |
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